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• W3047317549 abstract "Nature sound recordings have been collected for over a hundred years, with an exponential increase since the 1950s (Ranft 2004). Most such recordings were taken in order to describe and decipher animal communication. However, the sounds of animals reveal more than behaviour: they also reflect the structure and functioning of the ecosystem of which the animals are a part. The practice of deploying remote acoustic sensors in natural environments has been systematized under the term ‘passive acoustic monitoring’ (PAM), a technical term mostly used in marine acoustics but then employed in terrestrial and aquatic acoustics (Gillespie et al. 2009; Marques et al. 2012). Acoustic sensing has distinct advantages which make it complementary to other sampling modalities. Like camera trapping, acoustic sensing can be used on land or under water, in all type of habitats. An acoustic sensor has the advantage that it can capture a wide spatial range (often 360° and about 100 m in terrestrial habitats), and is much less affected by occlusion than imagery. It can also record continuously or regularly over a long time period and can collect information of a full assemblage of species as it captures all the sounds in the surrounding environnment. These properties ensure a high sampling effort with a rather low technical investment (Ciira wa Maina 2016; Hill et al. 2018). In many studies, acoustic data are analysed manually or with simple energy-based detectors, and with the goal of targeted monitoring of a single species (Dawson and Efford 2009; Gillespie et al. 2009; Digby et al. 2013). However, ambient sound recordings such as those obtained with automatic devices contain evidence for a long list of ecological information, such as: species absence/presence, population density, population structure, community structure, landscape architecture, animal phenology, reproduction period, migration period, species interactions or ecosystem functions. Many of these only become evident through large-scale studies, with analysis methods tailored to acoustic data. Benefiting from growth in recent decades of the scale of data capture and processing, the focus in acoustic monitoring can shift to broader ecosystem-level questions, while using audio as a prime source of evidence. This is the main goal of ecoacoustics (Sueur and Farina 2015). Ecoacoustic methods can cover all types of environment from deep sea to tropical forest, and are complementary to other biodiversity monitoring techniques such as camera trapping, LIDAR, satellite-based remote sensing or environmental DNA. Research in ecoacoustic methods has grown massively over the past 15 years, developing methodology in hardware devices, signal processing, machine learning and visualization (see Sugai et al. 2019, this issue, for a review). Particularly important is the move from ecoacoustics as a fundamental to an applied science, with such methods being deployed in practical conservation and ecosystem monitoring (Ciira wa Maina 2016; Gordon et al. 2019; Sertlek et al. 2019; Znidersic et al. 2020). Within the context of the United Nations Sustainable Development Goals (UN SDGs), ecoacoustic methods have already been demonstrated to contribute useful evidence, which can complement other evidence sources. Within SDG 14 ‘Life below water’ and SDG 15 ‘Life on land’, these include monitoring threatened species (Braune et al. 2008; Hill et al. 2018), invasive species (Grant and Grant 2010), poaching (Hill et al. 2018), noise pollution (both on land and below water) (Fairbrass et al. 2018; Sertlek et al. 2019), land degradation and mountain ecosystems (Helbig-Bonitz et al. 2015). Much of the recent technical progress has been at the level of signal analysis and sound classification, in particular the development of acoustic indices on the one hand, and the use of deep learning tools on the other (Stowell et al. 2019; Joly et al. 2019), and from signal processing engineering work on representations and transformations of audio data (Sueur et al. 2014; Phillips et al. 2018). At the sensor level, recent progress is in low-cost innovation (Hill et al. 2018), and the main challenge is now to have connected sensors so that data streams can be integrated continuously (Roch et al. 2017; Sethi et al. 2018) or to have integrated systems being able to run signal analyses and classification directly on board. Large-scale acoustic methods should now be transferred to application, and used more widely for conservation and management. It is now time to use ecoacoustics as a tool. Acoustic sensors should be included in large scale (i.e. national and international) monitoring programmes, in complementary fashion to other standard methods and in particular to design acoustic monitoring into long term programs. As cited above, there are many documented case studies and methodological developments that support this move. We are pleased to introduce this special issue of Remote Sensing in Ecology and Conservation on ecoacoustics, demonstrating across many different ecosystems the value and maturity of ecoacoustic methods. Methodologically, there are two broad paradigms in ecoacoustics, and they are reflected in this issue. One paradigm measures the acoustic diversity of a soundscape through the computation of acoustic indices: algorithmically straightforward and highly scalable, these indices yield evidence of biodiversity that is implicit, but holistic across many taxa. Sánchez-Giraldo et al. (2020) and Roca and Van Opzeeland (2019) conduct large-scale studies in very different ecological contexts – respectively in forests in the Columbian Andes, and underwater in the Southern Ocean – and quantify the reliability of such indices. Sánchez-Giraldo et al. (2020) tackle the widely encountered issue of the effect of rain noise on index computation, while Roca and Van Opzeeland (2019) reveal acoustic significant differences between distinct Antarctic marine habitats using a set of indices. Campos-Cerqueira et al. (2019) develop another type of index by extracting compressed data from a long-term spectrogram representation. This study is one of the first to test for the efficiency of conservation policy, supporting the idea that ecoacoustics should now conduct applied research. The second paradigm involves detecting or counting individual acoustic events, often limited to chosen target species. This offers a higher degree of selectivity, but can only be performed approximately when applied automatically at large scale. In a thorough review of passive acoustic monitoring techniques, Sugai et al. (2019) propose a set of good practices for designing the design of such automated surveys. This constitutes a crucial step towards the standardization of ecoacoustic data collection. Smith (2020) define a data sampling protocol suitable for very long duration (multi-year) acoustic monitoring, which focuses on seasonally varying patterns of peak acoustic activity. They demonstrate in a field study that it can produce comparable results as manual field transects, with less than a quarter of effective survey effort. Yip et al. (2019) demonstrate that sound level measurements can improve population density estimates, serving as a proxy measure for the distance between sound event and autonomous recording unit. Both methodological paradigms can be applied to monophonic or to multi-channel audio recordings. In either case, there will usually be multiple animals audible on any given audio track. Lin and Tsao (2019) provide a review and roadmap of source-separation methods including recent techniques that may help to disentangle overlapping sounds in monophonic recordings. Sumitani et al. (2020) demonstrate that interaction patterns among vocalizing individuals can be characterized with the aid of a dimension reduction algorithm coupled to a new compact microphone array, leading to automatic source localization. The projects represented in this volume review methods in use, introduce new tools and apply with success ecoacoustic principles in very different geographic and environmental contexts. Altogether, these contributions reveal that ecoacoustic evidence can now inform at large scale national and international biodiversity policy." @default.
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• W3047317549 title "Ecoacoustics: acoustic sensing for biodiversity monitoring at scale" @default.
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